Present day robot systems are required to meet stringent production schedules and targets. A malfunctioning of any critical component of the unit often makes this task not only difficult, but sometimes impossible. A prolonged equipment breakdown adversely affects the production output of that unit and sometimes, depending upon the flow of parts in the assembly line, adversely affects the performance of the manufacturing cells preceding and succeeding it.
Reasons for extended down-time intervals range from a lack of necessary training for the operator, tremendous variety and nature of problems that can arise, physical distance of the equipment from available help, to time lapses between occurrence and realization of the existence of a problem. To be able to address the above issues, it would be desirable to continuously and automatically monitor and diagnose problems, as they occur.
Meaningful conclusions can only be drawn from good data. As a result, it will be necessary to identify signals which will provide clues to sources of problems. Since the ongoing manufacturing process cannot be disrupted, it is important that any monitoring system not only track process conditions in real time, but that the tracking itself be non-disruptive.
Thus there is a need for a non-disruptive monitoring system which can assist an operator in analyzing and diagnosing problems.